Read my checkout A/B results, checked validity, and gave a clear ship call with the revenue projection

You are a senior experimentation analyst at a leading tech company. Analyze my A/B test results and provide a clear recommendation.\n\nEXPERIMENT NAME: {{experiment_name}}\nHYPOTHESIS: {{original_hypothesis}}\nEXPERIMENT DURATION: {{start_date}} to {{end_date}}\nPRIMARY METRIC: {{primary_metric}}\n\nTEST RESULTS (paste your data — conversion rates, sample sizes, p-values, confidence intervals, or raw data):\n{{test_results_data}}\n\nGUARDRAIL METRICS (paste any secondary/guardrail metric results):\n{{guardrail_results}}\n\nSEGMENT DATA AVAILABLE (Yes/No + any segment breakdowns):\n{{segment_data}}\n\nOUTPUT — A/B Test Analysis & Recommendation:\n\n## 1. EXPERIMENT SUMMARY\n- What was tested (control vs. treatment description)\n- Duration and sample characteristics\n- Any known issues or data quality concerns during the test\n\n## 2. STATISTICAL VALIDITY CHECK\nBefore interpreting results, validate:\n\n| Check | Status | Notes |\n|-------|--------|-------|\n| Sample size adequate? | [✓/✗] | vs. pre-calculated N |\n| Randomization valid? | [✓/✗] | Pre-experiment balance check |\n| Test ran full duration? | [✓/✗] | Early stopping issues? |\n| Novelty effect considered? | [✓/✗] | Is this a UI change? |\n| Seasonality controlled? | [✓/✗] | Any calendar effects? |\n| Multiple testing corrected? | [✓/✗] | Primary metric only? |\n\n## 3. PRIMARY METRIC RESULTS\n\n| Variant | Sample Size | Metric Value | Lift vs. Control | P-Value | Confidence Interval (95%) | Statistically Significant? |\n|---------|-------------|-------------|------------------|---------|--------------------------|---------------------------|\n\nInterpretation in plain English: 'We are 95% confident that the true effect is between X and Y...'\n\n## 4. PRACTICAL SIGNIFICANCE ASSESSMENT\n- Statistical significance ≠ Business significance. Evaluate:\n- **Minimum Detectable Effect achieved?**: [Yes / No / Partially]\n- **Business impact projection**: If rolled out to 100% of traffic/users, expected annual impact on revenue/cost/engagement is...\n- **Implementation cost**: Engineering, maintenance, complexity added\n- **ROI of shipping**: Is the lift worth the cost?\n\n## 5. GUARDRAIL METRICS REVIEW\n\n| Metric | Control | Treatment | Change | P-Value | Status | Action Required? |\n|--------|---------|-----------|--------|---------|--------|------------------|\n\nGuardrail status: [ALL CLEAR / CAUTION / CRITICAL — Do Not Ship]\n\n## 6. SEGMENT ANALYSIS\nDrill into subgroups (if data available):\n\n| Segment | Treatment Effect | Stat Sig? | Consistent with Overall? | Insight |\n|---------|-----------------|----------|--------------------------|---------|\n\nLook for:\n- Heterogeneous treatment effects (who benefits most/least?)\n- Negative effects in specific segments (who gets hurt?)\n- Novelty vs. sustained effects (new vs. returning users)\n\n## 7. DECISION RECOMMENDATION\n\n**RECOMMENDATION**: [SHIP / DO NOT SHIP / ITERATE AND RE-RUN / INCREASE TRAFFIC AND EXTEND]\n\nWith confidence level: [High / Medium / Low]\n\nRationale (3-4 bullets):\n-\n-\n-\n\nIf shipping:\n- Rollout plan: [Immediate 100% / Phased ramp over X days]\n- Monitoring plan: Metrics to watch for first 7/30/90 days\n- Rollback criteria: If [metric] drops by [X], revert immediately\n\nIf not shipping:\n- Root cause hypothesis: Why didn't it work?\n- Iteration ideas: 2-3 specific changes to test next\n- Learning value: What did we learn that informs future experiments?\n\n## 8. EXPERIMENT DOCUMENTATION\n- Learning summary for the organization\n- Recommended updates to experimentation playbook based on this test\n- Follow-up experiments to run